from ctypes import *
import math
import random
import cv2
import os


def sample(probs):
    s = sum(probs)
    probs = [a/s for a in probs]
    r = random.uniform(0, 1)
    for i in range(len(probs)):
        r = r - probs[i]
        if r <= 0:
            return i
    return len(probs)-1


def c_array(ctype, values):
    arr = (ctype*len(values))()
    arr[:] = values
    return arr


class BOX(Structure):
    _fields_ = [("x", c_float),
                ("y", c_float),
                ("w", c_float),
                ("h", c_float)]


class DETECTION(Structure):
    _fields_ = [("bbox", BOX),
                ("classes", c_int),
                ("prob", POINTER(c_float)),
                ("mask", POINTER(c_float)),
                ("objectness", c_float),
                ("sort_class", c_int)]


class IMAGE(Structure):
    _fields_ = [("w", c_int),
                ("h", c_int),
                ("c", c_int),
                ("data", POINTER(c_float))]


class METADATA(Structure):
    _fields_ = [("classes", c_int),
                ("names", POINTER(c_char_p))]


lib = CDLL(b"/home/chujie/git/darknet_example/libdarknet.so", RTLD_GLOBAL)
lib.network_width.argtypes = [c_void_p]
lib.network_width.restype = c_int
lib.network_height.argtypes = [c_void_p]
lib.network_height.restype = c_int

predict = lib.network_predict
predict.argtypes = [c_void_p, POINTER(c_float)]
predict.restype = POINTER(c_float)

set_gpu = lib.cuda_set_device
set_gpu.argtypes = [c_int]

make_image = lib.make_image
make_image.argtypes = [c_int, c_int, c_int]
make_image.restype = IMAGE

get_network_boxes = lib.get_network_boxes
get_network_boxes.argtypes = [c_void_p, c_int, c_int, c_float, c_float, POINTER(c_int), c_int, POINTER(c_int)]
get_network_boxes.restype = POINTER(DETECTION)

make_network_boxes = lib.make_network_boxes
make_network_boxes.argtypes = [c_void_p]
make_network_boxes.restype = POINTER(DETECTION)

free_detections = lib.free_detections
free_detections.argtypes = [POINTER(DETECTION), c_int]

free_ptrs = lib.free_ptrs
free_ptrs.argtypes = [POINTER(c_void_p), c_int]

network_predict = lib.network_predict
network_predict.argtypes = [c_void_p, POINTER(c_float)]

reset_rnn = lib.reset_rnn
reset_rnn.argtypes = [c_void_p]

load_net = lib.load_network
load_net.argtypes = [c_char_p, c_char_p, c_int]
load_net.restype = c_void_p

do_nms_obj = lib.do_nms_obj
do_nms_obj.argtypes = [POINTER(DETECTION), c_int, c_int, c_float]

do_nms_sort = lib.do_nms_sort
do_nms_sort.argtypes = [POINTER(DETECTION), c_int, c_int, c_float]

free_image = lib.free_image
free_image.argtypes = [IMAGE]

letterbox_image = lib.letterbox_image
letterbox_image.argtypes = [IMAGE, c_int, c_int]
letterbox_image.restype = IMAGE

load_meta = lib.get_metadata
lib.get_metadata.argtypes = [c_char_p]
lib.get_metadata.restype = METADATA

load_image = lib.load_image_color
load_image.argtypes = [c_char_p, c_int, c_int]
load_image.restype = IMAGE

rgbgr_image = lib.rgbgr_image
rgbgr_image.argtypes = [IMAGE]

predict_image = lib.network_predict_image
predict_image.argtypes = [c_void_p, IMAGE]
predict_image.restype = POINTER(c_float)


def classify(net, meta, im):
    out = predict_image(net, im)
    res = []
    for i in range(meta.classes):
        res.append((meta.names[i], out[i]))
    res = sorted(res, key=lambda x: -x[1])
    return res


def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.3):
    im = load_image(image, 0, 0)
    num = c_int(0)
    pnum = pointer(num)
    predict_image(net, im)
    dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, None, 0, pnum)
    num = pnum[0]
    if (nms):
        do_nms_obj(dets, num, meta.classes, nms)

    res = []
    for j in range(num):
        for i in range(meta.classes):
            if dets[j].prob[i] > 0:
                b = dets[j].bbox
                res.append((meta.names[i], dets[j].prob[i], (b.x, b.y, b.w, b.h)))
    res = sorted(res, key=lambda x: -x[1])
    free_image(im)
    free_detections(dets, num)
    return res


if __name__ == "__main__":
    save_result_in_one_file = False
    net = load_net(b"/home/chujie/git/darknet_example/cfg/yolov3-tiny.cfg",
                   b"/home/chujie/git/darknet_example/cfg/yolov3-tiny.weights", 0)
    meta = load_meta(b"/home/chujie/git/darknet_example/cfg/coco.data")
    if save_result_in_one_file:
        save_file = open("voc2012/result/result.txt", 'w')
    test_filelist = open("voc2012/ImageSets/Main/test_img_path.txt", 'r')
    color = (0, 0, 255)
    for image_full_path in test_filelist.readlines():
        if not save_result_in_one_file:
            save_file_single = open("voc2012/result/{}.txt".format(
                os.path.split(image_full_path)[-1].split('.')[0]), 'w')
        img = cv2.imread("{}".format(image_full_path).strip())
        # r = detect(net, meta, b"/home/chujie/PycharmProjects/data_tool/data/voc2012/JPEGImages/2011_000953.jpg")
        r = detect(net, meta, bytes("{}".format(image_full_path).strip().encode('utf-8')))
        for obj_info in r:

            class_name = str(obj_info[0], encoding='utf-8').replace(" ", "")
            rect = (obj_info[2][0], obj_info[2][1],
                    obj_info[2][2], obj_info[2][3])
            left = int(rect[0] - rect[2] / 2)
            top = int(rect[1] - rect[3] / 2)
            right = int(left + rect[2])
            bottom = int(top + rect[3])
            if not save_result_in_one_file:
                single_content = "{} {} {} {} {} {}\n".format(class_name,
                                                              obj_info[1], left, top, right, bottom)
                print(single_content)
                save_file_single.write(single_content)
            else:
                content = "{} {} {} {} {} {} {}\n".format(
                    image_full_path.strip(), class_name,
                    obj_info[1], left, top, right, bottom)
                print(content)
                save_file.write(content)
            cv2.rectangle(img, (left, top),
                          (right, bottom), color, 2)
            cv2.putText(img, str(obj_info[0], encoding='utf-8').replace(" ", ""),
                        (left, top - 5),
                        cv2.FONT_HERSHEY_SIMPLEX, 0.8, color, 2)
        cv2.imwrite(image_full_path.replace("JPEGImages", "yolo_detect_result"), img)
        if not save_result_in_one_file:
            save_file_single.close()
    if save_result_in_one_file:
        save_file.close()
    test_filelist.close()
    

